Object Learning Through Active Exploration

Serena Ivaldi, Sao Mai Nguyen, Natalia Lyubova, Alain Droniou, Vincent Padois, David Filliat, Pierre-Yves Oudeyer, Olivier Sigaud
<span title="">2014</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kglkdqfduff75jrr466lsjoe34" style="color: black;">IEEE Transactions on Autonomous Mental Development</a> </i> &nbsp;
This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social
more &raquo; ... parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tamd.2013.2280614">doi:10.1109/tamd.2013.2280614</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3376fjdbczbypjhag625cwkssu">fatcat:3376fjdbczbypjhag625cwkssu</a> </span>
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